Respiratory distress syndrome prediction at birth by optical skin maturity assessment and machine learning models for limited-resource settings: a development and validation study

• Published in: Frontiers in pediatrics Vol. 11; p. 1264527
• Main Authors: Reis, Zilma Silveira Nogueira, Pappa, Gisele Lobo, Nader, Paulo de Jesus H., do Vale, Marynea Silva, Silveira Neves, Gabriela, Vitral, Gabriela Luiza Nogueira, Mussagy, Nilza, Norberto Dias, Ivana Mara, Romanelli, Roberta Maia de Castro
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 15-11-2023
• Subjects: childbirth; machine learning; newborn; Pediatrics; prematurity; respiratory distress syndrome; skin physiological phenomena
• ISSN: 2296-2360; 2296-2360
• DOI: 10.3389/fped.2023.1264527

BackgroundA handheld optical device was developed to evaluate a newborn's skin maturity by assessing the photobiological properties of the tissue and processing it with other variables to predict early neonatal prognosis related to prematurity. This study assessed the device's ability to predict respiratory distress syndrome (RDS).MethodsTo assess the device's utility we enrolled newborns at childbirth in six urban perinatal centers from two multicenter single-blinded clinical trials. All newborns had inpatient follow-up until 72 h of life. We trained supervised machine learning models with data from 780 newborns in a Brazilian trial and provided external validation with data from 305 low-birth-weight newborns from another trial that assessed Brazilian and Mozambican newborns. The index test measured skin optical reflection with an optical sensor and adjusted acquired values with clinical variables such as birth weight and prenatal corticoid exposition for lung maturity, maternal diabetes, and hypertensive disturbances. The performance of the models was evaluated using intrasample k-parts cross-validation and external validation in an independent sample.ResultsModels adjusting three predictors (skin reflection, birth weight, and antenatal corticoid exposure) or five predictors had a similar performance, including or not maternal diabetes and hypertensive diseases. The best global accuracy was 89.7 (95% CI: 87.4 to 91.8, with a high sensitivity of 85.6% (80.2 to 90.0) and specificity of 91.3% (95% CI: 88.7 to 93.5). The test correctly discriminated RDS newborns in external validation, with 82.3% (95% CI: 77.5 to 86.4) accuracy. Our findings demonstrate a new way to assess a newborn's lung maturity, providing potential opportunities for earlier and more effective care.Trial registrationRBR-3f5bm5 (online access: http://www.ensaiosclinicos.gov.br/rg/RBR-3f5bm5/), and RBR-33mjf (online access: https://ensaiosclinicos.gov.br/rg/RBR-33rnjf/).